We look for the optimal digital pulses design, which is similar to invariant-based STA for realizing robust quantum control. The optimal design is indeed a combinational optimization problem, being equivalent to dynamic programming, which is no longer analytically solvable. As artificial intelligence approach, Reinforcement Learning is a well-known tool for system control [1]}, and deep learning has been developed for conquering complicated tasks in many areas [2]}, [3]}, [4]}, [5]}, later applied in studying physics [6]}, [7]}, [8]}, [9]}, [10]}, [11]}. The framework of deep learning can be combined with reinforcement learning, searching control pulses for quantum state preparation [12]}, [13]}, gate operation [14]}, and quantum Szilard engine [15]}. Since recent researches have employed Deep Reinforcement Learning (DRL) for quantum control [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, we are inspired to investigate the connection between DRL and STA. An optimistic expectation is that one can extend STA's concept, introducing DRL as a new technique if it learns the features of STA protocols.
